Abstract P190: Do We Combine the Data for Analysis from VENUS and MARS? The Time Has Come for Consistent Sex-Based Analysis of Health Outcome Data
Bibliographic record
Abstract
Background Previous investigations by our group have consistently identified important sex differences in HRQOL outcomes of patients with CAD with women reporting poorer HRQOL compared with men. The purpose of this study was to extend our previous work to determine whether sex differences and/or associations in bivariate analyses may provide insight in the modeling of health outcomes data. Method A descriptive analysis of the variables was performed. Sex differences on all variables were examined using t test and Chi-square analyses. The relationships between all clinical, demographic, socio-demographic and HRQOL outcome variables were examined stratified by sex. Results 7062, 1- year HRQOL questionnaires were collected on patients catheterized between Jan 2006 and Dec 2009. 20.8% (1468 of 7062) were from women. Statistically significant sex differences were noted in 10/23 clinical and all 8 of the sociodemographic variables measured. A critical sex difference in the nature of the relationship between depression scores and age was identified. Whereas a quadratic relationship was seen in the men's group, the relationship in the women's group was cubic (figure 1). This implies that analyzing data by including sex, age, and depression scores in the same model will in essence sacrifice the unique nature of the relationship for at least one sex. Conclusions Our data suggests that sex-based analyses should be conducted particularly when modeling predictors of HRQOL outcome. Failing to do so may result in misleading conclusions that will miss opportunities to intervene early in clinically treatable circumstances and to improve the outcomes of men and women with CAD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.266 | 0.553 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".